Abstract

Photos posted by consumers on social media, like Instagram, often include brands. Despite the substantial increase in such photos, there have been few investigations into how prospective consumers respond to this visual UGC. We begin to address this gap by investigating the role of the color compositions of visual UGC in consumer response. Consumer response is operationalized as the click-rate for a photo by a consumer when it is curated on the online site of the brand that it includes. This is the proportion of visitors who click on it for an enlarged view. Composition is operationalized as the specific combination of levels of the photo’s color attributes: hue, chroma, and brightness. Our goal is to identify the color compositions of photos, ceteris paribus, which get more clicks when they are curated. Data for our investigation comes from clicks over a one-year period on photos posted on Instagram curated by fifteen brands in six product categories on their sites. We assume Beta distributed proportions and calibrate a Beta regression using MCMC methods for our investigation.

We find that click-rates are higher for photos that include higher proportions of green and lower proportions of red and cyan. We also find that chroma of red and blue are higher in photos with higher click-rates. Findings from our research led the sponsoring firm to modify its proprietary curation algorithm for client brands. The firm informed us that, post-modification, there has been a substantial increase in click-rates of curated photos for brands in several categories.

Keywords

Notes

Acknowledgements

The authors are grateful to Pradeep Chintagunta and Sanjog Misra for a very constructive and helpful review process. They would also like to thank two anonymous reviewers for their insightful comments. They also express their gratitude to the agency’s founders for providing the data and being always readily available for discussions and clarifications during the research. They also thank Lakshman Krishnamurthi, Don Lehmann, Puneet Manchanda, Anita Rao and attendees of the Big Data Marketing Analytics Conference at the University of Chicago’s Gleacher Center on October 31, 2014, for comments during the early stages of this research.

Appendix

Cobb-Douglas Formulation of the Model

In response to a referee’s concern that, “our additively separable specification” of the Logit link in our model does not truly capture the effects of color compositions, we estimated the model using the following Cobb-Douglas type of specification of the link:

This specification allows all aspects of each hue’s proportion, chroma, and brightness, to affect the role of the other hues as in a typical Cobb-Douglas formulation. We calibrated this model as well using MCMC methods and present the results in Appendix Table 7. A comparison of the significant effects from the two specifications suggests that:

1.

Hues: The direction of the effects of red, green, and cyan hues is unchanged – both red and cyan continue to have a negative effect while green has a positive effect.

2.

Chromas: The chromas of red and blue continue to have a positive effect.

3.

Additional insights: One advantage of the Cobb-Douglas formulation is that it permits us to include all the colors rather than relying on Violet as the base. We therefore find that violet also has a positive effect on click-rates. Additionally, this specification indicates that the brightness of cyan has a small positive effect.

Table 7

Parameter estimates of cobb-douglass specification

Variable

Posterior Summary

Mean

2.50%

97.50%

Colors

Reda

-0.056

-0.086

-0.024

Chroma of Reda

0.001

0.000

0.002

Brightness of Red

0.000

-0.001

0.001

Yellow

0.016

-0.038

0.055

Chroma of Yellow

0.000

-0.001

0.001

Brightness of Yellow

0.000

-0.001

0.000

Greena

0.046

0.011

0.086

Chroma of Green

0.001

-0.001

0.002

Brightness of Green

-0.001

-0.001

0.000

Cyana

-0.036

-0.060

-0.009

Chroma of Cyan

-0.002

-0.003

0.000

Brightness of Cyana

0.001

0.000

0.001

Blue

-0.018

-0.040

0.004

Chroma of Bluea

0.001

0.000

0.002

Brightness of Blue

0.000

0.000

0.000

Violeta

0.050

0.025

0.075

Chroma of Violet

-0.001

-0.002

0.000

Brightness of Violet

-0.001

-0.001

0.000

Filters

Amaro

-0.148

-0.239

-0.057

Hefe

0.047

-0.092

0.174

Hudson

0.051

-0.087

0.177

lo-fi

0.140

0.045

0.229

Mayfair

-0.072

-0.166

0.027

Nashville

-0.025

-0.169

0.126

Rise

0.003

-0.103

0.111

sierra

-0.222

-0.368

-0.071

Valencia

-0.096

-0.174

-0.022

x-pro ii

0.052

-0.042

0.149

Other

0.030

-0.047

0.107

Number of photos in gallery

0.112

0.058

0.167

Precision of Gallery random effectsa

8.639

8.319

8.931

Precision of the Beta Distributiona

11.480

8.999

14.300

a95% posterior credible interval does not include zero

Thus, while the findings from the additively-separable specification remain essentially unchanged, we obtain additional insights from the new specification. We would like to thank the anonymous reviewer for raising the issue prompting us to investigate the alternative specification.

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